Robotic Process Automation vs Machine Learning: What's best for P2P?

In a recent EBG webinar, How to Leverage AI, Machine Learning and RPA in P2P & the Finance Function Today: Value Drivers & Limitations, Dr. Bernhard Schaffrik, Principal Analyst with Forrester Consulting, and Magnus Bergfors, Global Business Director with Basware, discuss the role and benefits of artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA) in your P2P and Finance functions to help guide your decision-making throughout the automation journey.

What are companies saying about AI and ML?

Forrester opened the webinar by presenting the results of a survey conducted with approximately 3,000 business decision-makers involved in big data and analytics across industries, company sizes, and regions worldwide. Participants were asked, “What are your organization’s plans to use artificial intelligence (AI)—specifically machine learning (ML) and deep learning?”

The findings showed that more than 20% of respondents have already implemented AI and are expanding their initiatives, while another 20% are in the early stages of adoption. In total, nearly half of the surveyed organizations are already using AI and ML technologies. The same audience was then asked, “What could be, or are, the benefits for your organization in using AI and ML technologies?” The top four responses suggest that companies are gradually shifting their focus from cost efficiency and bottom-line reduction toward a more outcome-driven approach—where revenue growth has become a leading priority enabled by AI and ML.

1. Increase the automation of internal processes
2. Improve overall operational efficiency
3. Improve customer experience
4. Increase revenue growth

What is Robotic Process Automation (RPA)?

Robotic process automation (RPA) combines two key elements of software innovation: robotics and automation. RPA is a technology that records users performing repetitive tasks and generates scripts that allow software robots to execute those tasks automatically.

Recognized as one of the most effective technologies for procure-to-pay (P2P) operations, RPA represents only one part of the broader innovation landscape. Often described as a virtual workforce, RPA integrates with business applications and replicates desktop actions to perform tasks such as maintaining vendor databases, acknowledging receipt of goods, resolving price discrepancies, setting payment dates, updating the general ledger, or issuing checks. However, RPA is not the only technology organizations use to drive efficiency in their P2P operations. AI and ML also provide critical support to procurement and finance teams, helping them enhance strategic value and enable future growth.

What’s the difference between Robotic Process Automation and Machine Learning?

While both Robotic Process Automation (RPA) and Machine Learning (ML) fall under the broader umbrella of automation, they serve very different purposes. RPA is rule-based and excels at automating repetitive, predictable tasks such as data entry, invoice processing, or updating vendor records. It replicates human actions in software systems but cannot adapt when conditions change beyond its predefined rules.

Machine Learning, on the other hand, enables systems to learn from data and improve over time. ML can recognize patterns, make predictions, and handle complex, variable scenarios that RPA cannot manage on its own. In practice, ML complements RPA by providing the intelligence needed for decision-making in ambiguous or evolving situations, turning simple automation into a more strategic, adaptive solution for finance and P2P operations.

What do RPA tools lack?

Since RPA is less of a business process and more of a labor replacement for arduous manual tasks, as companies upgrade their legacy systems, existing RPA tools created to make older systems more efficient quickly become outdated and fall flat beyond the initial cost savings associated with reducing manual tasks.

For example, RPA works well for high-volume tasks that are repetitive in nature and can be automated using very specific business rules, but what happens when the parameters change? The robots must relearn what to do, so while RPA works great for some tasks within the P2P process (especially for the SMB market), it doesn’t scale. Global enterprises with complex processes and often a result of multiple mergers and acquisitions, need more to address their needs.

Intelligent Automation - More than just RPA

For tasks associated with complex processes, it’s important to consider intelligent automation. At Basware, we are continuously looking for ways to increase touchless processing through smart processing, artificial intelligence, and machine learning. To define the differences, let’s look at a few specific examples.

• Invoice and PO matching – At Basware, we match more PO and invoice scenarios than any of our competitors through the logic built into our solutions, minimizing the number of exceptions AP staff have to deal with. If you rely purely on RPA and it’s a simple transaction with maybe one or two items or if everything on the invoice matched the PO exactly, then it would all work seamlessly by just relying on RPA.
  
  But when you get into the more complex scenarios with multiple items, or there is line-item details on the PO but none on the invoice, RPA can’t solve the need for human intervention to resolve the discrepancy. However, Basware can handle all those complex scenarios with ease. We do the same with coding – our SmartCoding uses AI and ML to leverage historical transactions to suggest coding even for non-PO invoices.

• SmartPDF – While most of our competitors use OCR to extract data, we first determine whether a PDF invoice is machine readable, a native PDF, or if it’s an image PDF. If it’s a machine-readable PDF, there is no need to go through the process of making it into an image and then use OCR. You’ll just lose data accuracy. But by introducing an AI component to our SmartPDF offering, the AI automatically maps the fields between the format in which the supplier sent the invoice and the format in which the buyer can receive it – completely taking out the human effort needed.

• Predictive Analytics – We also leverage AI and machine learning on the analytics side of the solution. Predictive and prescriptive analytics help the modern finance organization by enabling users to use data from across their P2P cycle so they can analyse the probable outcomes of processes, make better decisions, and drive KPIs.

While these are just a few examples, Basware solutions are designed to minimize manual effort, drive touchless processing to reduce exceptions, and help AP professionals use their limited resources more strategically. All thanks to the combinative effort of ML, AI, and advanced automation.

Revenue growth and other benefits powered by AI & ML

With predictive intelligence, the journey towards delivering AI-powered insights that give our customers a distinct advantage is happening right now. With this approach, AI and ML are no longer buzzwords, but deliver measurable impact on P2P operations. With this elevated level of insight, customers reduce the cost of operations, spend smarter, and build strong business relationships with suppliers – securing long-term savings and growth.